Accurate Binding Free Energy Predictions in Fragment Optimization

Abstract

Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

Fragment optimization for GPCRs by molecular dynamics free energy calculations: Probing druggable subpockets of the A 2A adenosine receptor binding site

“…The lower correlation with experiment obtained for two series of adenine-derived ligands may reflect that these were of lead-like size and interacted with the flexible extracellular loops whereas the fragments considered in this work had limited conformational flexibility and extended into a relatively rigid TM region. Interestingly, two recent benchmarks of binding free energy calculations for a large number of soluble targets showed a similar trend 10, 39 . It should be noted that access to information regarding the binding mode for a representative ligand was likely a key contributor to the accuracy of the MD/FEP calculations in all cases.…”

“…In either case, access to high-resolution crystal structures of fragments bound to the target often makes crucial contributions to the optimization process 7 . Whereas computational methods for structure-based ligand design are routinely used for drug-sized molecules 9 , applications of such approaches to fragment optimization have been more scarce 10 . The fact that fragments are weak ligands, only occupy a small fraction of the binding site, and may have multiple binding modes adds extra levels of complexity that are challenging to predict with simplified models such as empirical scoring functions.…”

“…Furthermore, scoring functions developed for computer-aided ligand design have been parameterized based on drug-like compounds, and it has been suggested that these may not be suitable for fragment ligands 11, 12 . Molecular dynamics (MD) simulations in combination with alchemical free energy methods, which explicitly consider contributions to binding from conformational flexibility and interactions with water molecules, could provide a rigorous approach to guide fragment optimization 13 , but this technique has only recently been applied to FBLD 10 . Accurate predictions of relative binding affinities for analogs to ligands identified by fragment screening could improve the efficiency of FBLD, further establishing this approach as a groundbreaking strategy for early drug development.…”

Adaptive Landscape Flattening Accelerates Sampling of Alchemical Space in Multisite λ Dynamics

“…MS λ D has historically focused on modifying small functional groups. 6,7,20 While such calculations have many applications, the ability to look at larger perturbations with landscape flattening opens up new possibilities in fragment-based drug design 13,36 and in exploring the effects of protein mutation on drug binding. 37–39 …”

Shedding light on the puzzle of drug-membrane interactions: Experimental techniques and molecular dynamics simulations

“…The disadvantage of simulations is their computational cost, which prohibits the possibility of performing screening studies with MD simulations. This might change in the future, as a recent example using MD methods in fragmentbased drug design has demonstrated that MD might be possible for screening studies [10]. This review will provide a brief overview of the most common experimental techniques applied in the study of drug-membrane interactions.…”